BUG: to_timedelta with ArrowDtype(pa.duration)

doc/source/whatsnew/v2.1.0.rst

@@ -504,6 +504,7 @@ Timedelta
 - :meth:`TimedeltaIndex.map` with ``na_action="ignore"`` now works as expected (:issue:`51644`)
 - Bug in :class:`TimedeltaIndex` division or multiplication leading to ``.freq`` of "0 Days" instead of ``None`` (:issue:`51575`)
 - Bug in :class:`Timedelta` with Numpy timedelta64 objects not properly raising ``ValueError`` (:issue:`52806`)
+- Bug in :func:`to_timedelta` converting :class:`Series` or :class:`DataFrame` containing :class:`ArrowDtype` of ``pyarrow.duration`` to numpy ``timedelta64`` (:issue:`54298`)
 - Bug in :meth:`Timedelta.__hash__`, raising an ``OutOfBoundsTimedelta`` on certain large values of second resolution (:issue:`54037`)
 - Bug in :meth:`Timedelta.round` with values close to the implementation bounds returning incorrect results instead of raising ``OutOfBoundsTimedelta`` (:issue:`51494`)
 - Bug in :meth:`arrays.TimedeltaArray.map` and :meth:`TimedeltaIndex.map`, where the supplied callable operated array-wise instead of element-wise (:issue:`51977`)

pandas/core/tools/timedeltas.py

@@ -23,6 +23,7 @@
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.dtypes import ArrowDtype
 from pandas.core.dtypes.generic import (
     ABCIndex,
     ABCSeries,
@@ -31,6 +32,7 @@
 from pandas.core.arrays.timedeltas import sequence_to_td64ns
 
 if TYPE_CHECKING:
+    from collections.abc import Hashable
     from datetime import timedelta
 
     from pandas._libs.tslibs.timedeltas import UnitChoices
@@ -242,17 +244,23 @@ def _coerce_scalar_to_timedelta_type(
 
 
 def _convert_listlike(
-    arg, unit=None, errors: DateTimeErrorChoices = "raise", name=None
+    arg,
+    unit: UnitChoices | None = None,
+    errors: DateTimeErrorChoices = "raise",
+    name: Hashable | None = None,
 ):
     """Convert a list of objects to a timedelta index object."""
-    if isinstance(arg, (list, tuple)) or not hasattr(arg, "dtype"):
+    arg_dtype = getattr(arg, "dtype", None)
+    if isinstance(arg, (list, tuple)) or arg_dtype is None:
         # This is needed only to ensure that in the case where we end up
         #  returning arg (errors == "ignore"), and where the input is a
         #  generator, we return a useful list-like instead of a
         #  used-up generator
         if not hasattr(arg, "__array__"):
             arg = list(arg)
         arg = np.array(arg, dtype=object)
+    elif isinstance(arg_dtype, ArrowDtype) and arg_dtype.kind == "m":
+        return arg
 
     try:
         td64arr = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0]

pandas/tests/tools/test_to_timedelta.py

@@ -306,3 +306,12 @@ def test_from_numeric_arrow_dtype(any_numeric_ea_dtype):
     result = to_timedelta(ser)
     expected = Series([1, 2], dtype="timedelta64[ns]")
     tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("unit", ["ns", "ms"])
+def test_from_timedelta_arrow_dtype(unit):
+    # GH 54298
+    pytest.importorskip("pyarrow")
+    expected = Series([timedelta(1)], dtype=f"duration[{unit}][pyarrow]")
+    result = to_timedelta(expected)
+    tm.assert_series_equal(result, expected)